SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

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Showing 1361–1370 of 4891 papers

TitleStatusHype
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient NoiseCode0
Learning a Mini-batch Graph Transformer via Two-stage Interaction AugmentationCode0
Domain Reduction Strategy for Non Line of Sight ImagingCode0
Learning-based model augmentation with LFRsCode0
DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional NetworksCode0
Learning Decision Trees and Forests with Algorithmic RecourseCode0
Accelerated Alternating Projections for Robust Principal Component AnalysisCode0
Finding Influential Training Samples for Gradient Boosted Decision TreesCode0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified